Anthropogenic sea-level rise (SLR) is predicted to impact, and, in many cases, displace, a large proportion of the population via inundation and heightened SLR-related hazards. With the global coastal population projected to surpass one billion people this century, SLR might be among the most costly and permanent future consequences of climate change. In this Review, we synthesize the rapidly expanding knowledge of human mobility and migration responses to SLR, providing a coherent roadmap for future SLR research and associated policy. While it is often assumed that direct inundation forces a migration, we discuss how mobility responses are instead driven by a diversity of socioeconomic and demographic factors, which, in some cases, do not result in a migration response. We link SLR hazards with potential mechanisms of migration and the associated governmental or institutional policies that operate as obstacles or facilitators for that migration. Specific examples from the USA, Bangladesh and atoll island nations are used to contextualize these concepts. However, further research is needed on the fundamental mechanisms underlying SLR migration, tipping points, thresholds and feedbacks, risk perception and migration to fully understand migration responses to SLR.

The exposure of populations to sea-level rise (SLR) is a leading indicator assessing the impact of future climate change on coastal regions. SLR exposes coastal populations to a spectrum of impacts with broad spatial and temporal heterogeneity, but exposure assessments often narrowly define the spatial zone of flooding. Here we show how choice of zone results in differential exposure estimates across space and time. Further, we apply a spatio-temporal flood-modeling approach that integrates across these spatial zones to assess the annual probability of population exposure. We apply our model to the coastal United States to demonstrate a more robust assessment of population exposure to flooding from SLR in any given year. Our results suggest that more explicit decisions regarding spatial zone (and associated temporal implication) will improve adaptation planning and policies by indicating the relative chance and magnitude of coastal populations to be affected by future SLR.

Lead is a developmental neurotoxicant in wide industrial use that was once broadly distributed in the environment. The extent of the US population exposed in early life to high levels of lead is unknown, as are the consequences for population IQ. Serial, cross-sectional blood–lead level (BLL) data from National Health and Nutrition Examination Survey (NHANES), a nationally representative sample of US children aged 1 to 5 ( n = 11,616) from 1976 to 1980 to 2015 to 2016 was combined with population estimates from the US Census, the Human Mortality Database, and the United Nations. NHANES and leaded gasoline consumption data were used to estimate BLLs from 1940 to 1975. We estimated the number and proportion of people that fall within seven BLL categories (53%) had BLLs above 5 µg/dL in early life (±2.84 million [80% CI]), over 54 million (>17%) above 15 µg/dL, and over 4.5 million (>1%) above 30 µg/dL (±0.28 million [80% CI]). BLLs greater than 5 µg/dL were nearly universal (>90%) among those born 1951 to 1980, while BLLs were considerably lower than 5 µg/dL among those born since 2001. The average lead-linked loss in cognitive ability was 2.6 IQ points per person as of 2015. This amounted to a total loss of 824,097,690 IQ points, disproportionately endured by those born between 1951 and 1980.

Small area and subnational population projections are important for understanding long-term demographic changes. I provide county-level population projections by age, sex, and race in five-year intervals for the period 2020–2100 for all U.S. counties. Using historic U.S. census data in temporally rectified county boundaries and race groups for the period 1990–2015, I calculate cohort-change ratios (CCRs) and cohort-change differences (CCDs) for eighteen five-year age groups (0–85+ ), two sex groups (Male and Female), and four race groups (White NH, Black NH, Other NH, Hispanic) for all U.S counties. I then project these CCRs/CCDs using ARIMA models as inputs into Leslie matrix population projection models and control the projections to the Shared Socioeconomic Pathways. I validate the methods using ex-post facto evaluations using data from 1969–2000 to project 2000–2015. My results are reasonably accurate for this period. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States.